Development of a Machine Vision System for Damage Detection in Hexagon Nuts Sorting Machine

This study demonstrates the design and implementation of an automatic sorting machine for identifying damage in M12, M14, and M16 hexagon nuts. The machine uses the OpenCV library for image processing, PLCs for controlling mechanical components, and Visual Studio for development. The system aims to enhance efficiency in industrial hexagon nut sorting, featuring a bowl feeder, conveyor system, proximity sensors, and a webcam to capture images processed by algorithms in Visual Studio. The PLC controls the sorting mechanism, separating defective hexagon nuts and communicating via Modbus RTU over RS485. The system converts RGB images to grayscale, simplifying image processing by focusing on brightness variations. The system identifies distinct area ranges for each hexagon nut size, facilitating precise sorting. Experimental results show that larger hexagon nuts require stricter quality control because of lower acceptable damage thresholds. The vision system improves sorting accuracy and ensures only high-quality hexagon nuts proceed to production. Early damage detection prevents defective products from reaching consumers, saving time and resources. Successfully integrated into an automatic classifying machine, this system demonstrates the benefits of machine vision and automation in manufacturing, leading to improved quality control and cost savings. The overall sorting accuracy of 96.7% underscores the system’s reliability and precision, suggesting its significant potential for enhancing quality control processes in industrial settings.